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"A server for centralized logging and log management" matching MCP servers:

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    Provides comprehensive logging and monitoring capabilities for MCP services with real-time log tailing, advanced search, error analysis, and anomaly detection. Enables centralized log aggregation, correlation tracking, and health monitoring across all MCP ecosystem services.
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    MCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.
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    An MCP server for AI-powered log analysis that enables parsing, searching, and debugging across nine log formats directly within Claude. It features automated error extraction, sensitive data scanning, and streaming support for large log files.
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    Enables AI assistants to automatically inspect and analyze application runtime log files for debugging and troubleshooting. Supports monitoring multiple log directories simultaneously with tools for listing, reading, searching, and paginating through log files.
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    Enables LLMs to analyze logs by extracting patterns, redacting secrets, and providing token-efficient summaries from files, Docker containers, or journald.
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    Metered log ingestion server for autonomous agents, allowing NDJSON log posting with USDC payment per line on Base L2. Agents can freely tail and search their own logs with configurable retention tiers.
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    MIT
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    MCP server for collecting and analyzing CLI/web server error logs. Enables watching log files/directories, parsing common error patterns, and querying/analyzing logs through natural language.
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    MIT
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    Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.
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    Compresses log files into templates and statistics using Drain3, and exposes them to AI assistants via an MCP server for efficient log monitoring and anomaly detection.
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    An MCP server that gives AI assistants tools to analyze Salesforce Apex debug logs for performance bottlenecks, slow methods, and governor limit usage.
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    BSD 3-Clause
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    An MCP server for unified cost tracking and analysis across AWS, OpenAI, and Anthropic. It enables users to query expenditures, compare costs across providers, and analyze usage trends through natural language.
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    MIT
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    Agentic tool that looks for statistical variations in conversation structure and logs unusual events to a SQLite database. Built using the Model Context Protocol (MCP), this system is designed to be used with Claude Desktop or other MCP-compatible clients.
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    MIT